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使用偏态指标的分类分析稳健性:II. MAXCOV程序的蒙特卡罗研究。

Robustness of taxometric analysis with skewed indicators: II. A Monte Carlo study of the MAXCOV procedure.

作者信息

Haslam N, Cleland C

机构信息

Department of Psychology, New School for Social Research, New York, NY 10003, USA.

出版信息

Psychol Rep. 1996 Dec;79(3 Pt 1):1035-9. doi: 10.2466/pr0.1996.79.3.1035.

Abstract

A small Monte Carlo study was conducted to determine whether MAXCOV analysis, a taxometric method for testing between discrete ("taxonic") and continuous models of latent variables, is robust when indicators of the latent variable are skewed. Analysis of constructed data sets containing three levels of skew indicated that the MAXCOV procedure is unlikely to yield spurious findings of taxonicity even when skewness is considerable. However, care must be taken to distinguish low base-rate taxonic variables from skewed nontaxonic variables.

摘要

开展了一项小型蒙特卡洛研究,以确定MAXCOV分析(一种用于检验潜在变量的离散(“类属”)模型和连续模型的分类法)在潜在变量指标存在偏态时是否稳健。对包含三个偏态水平的构建数据集进行分析表明,即使偏态程度相当大,MAXCOV程序也不太可能产生虚假的类属结果。但是,必须注意区分低基础率的类属变量和偏态的非类属变量。

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